Fraunhofer Singapore

Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping.

The current virtual reality (VR) techniques develop immersive environments via inducing illusions to our sense. Nowadays, most of VR focuses on inducing visual illusion. Hence, visual is the most important input channel for experiencing and exploring the VR environments. Recently, extensive research efforts have been put on eye-tracking studies. However, the development and growing trends of the VR-based eye-tracking studies are unrevealed due to the lack of a systematic literature review on it. In this study, we reviewed related literature from 2000 to 2019 and summarized them into two main categories, including eye tracking methods and eye-tracking-enabled applications, such as tracking gaze points to manipulate the VR environment, measuring user states, and evaluating the usability of VR based on eye-tracking data. Based on the literature review, we can find that eye-tracking can assist in developing adaptive VR systems and enhance users experience. While comparing with 2D environments, immersive VR environment still requires more deep studies in eye-tracking.

Artificial intelligence is being increasingly applied in manufacturing to maximize industrial productivity. Image clustering, as a fundamental research direction in unsupervised learning, has been used in various fields. Since no label information is required in clustering, it can perform a preliminary analysis of the data while saving lots of manpower. In this paper, we propose a novel end-to-end clustering network called Self-supervised Pairing Image Clustering (SPIC) for industrial application, which produces clustering prediction for input images in an advanced pair classification network. For training this network, a self-supervised pairing module is built to form balanced pairs accurately and efficiently without label information. Since the existence of trivial solutions cannot be avoided in most of unsupervised learning methods, two additional information theoretic-constraints regularize the training that ensures the clustering prediction to be unambiguous and close to the real data distribution during training. Experimental results indicate that the proposed SPIC outperforms the state-of-art approaches on manufacturing datasets–NEU and DAGM. It also shows the execellent generalization capability on other genral public datasets, such as MNIST, Omniglot, CIFAR10, and CIFAR100.